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Erschienen in:
2011 | OriginalPaper | Buchkapitel
Identifying Oligomeric Proteins Based on a Self-constructed Dataset
verfasst von : Tong Wang, Wenan Tan, Lihua Hu
Erschienen in: Advanced Research on Computer Education, Simulation and Modeling
Verlag: Springer Berlin Heidelberg
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Oligomeric proteins are very common in nature. They can be divided into two classes: homo-oligomers and hetero-oligomers. In this paper, a new method for the prediction of oligomeric protein types is proposed based on a self-constructed dataset. This stringent benchmark data set were screened strictly in which none of proteins has ≥60% pairwise sequence identity to any other in the same subset. DC (Dipeptide Composition) is used as sequence encoding scheme for the construction of decision system. A supervised linear DR (Dimensionality Reduction) algorithm, the so-called LDA (Linear Discriminant Analysis) is introduced to reduce the decision system, which can be used to classify new objects. The results thus obtained in predicting the types of oligomeric proteins are quite encouraging.